Analyses for ‘Neural Correlates of Stressor Controllability in Humans’
Laura E. Meine & Jana Meier
2020-10-27
1 Script Overview
Built with R 3.6.2
This script runs analyses on data from the SPECTRE study. Specifically, it investigates different read-outs as a function of whether participants experienced controllable or uncontrollable stress (or no stress). Measurements under investigation comprised participant ratings (stressor aversiveness, perceived control, stress, helplessness), reaction times, correct responses, and heart rates.
Here we use the following abbreviations: controllable stress = con; uncontrollable stress = noc, baseline = bas.
Linear mixed-effects models were constructed based on the tutorial referenced in Singmann & Kellen, 2019: https://cran.r-project.org/web/packages/afex/vignettes/afex_mixed_example.html
2 Sample Descriptives
N = 45 participants aged 19-30 took part in this study (46.67 % female, age: M = 25, SD = 3.05).
3 Manipulation Checks
3.1 Version Counterbalancing
##
## 1 2 3 4 5 6
## 8 8 8 8 6 73.2 Yoking of Stress Durations
##
## Paired t-test
##
## data: stressDur$total_con_stress_dur and stressDur$total_noc_stress_dur
## t = 6.3263, df = 44, p-value = 1.117e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.178446 2.280295
## sample estimates:
## mean of the differences
## 1.72937##
## Cohen's d
##
## d estimate: 1.004407 (large)
## 95 percent confidence interval:
## lower upper
## 0.559817 1.448996There was a significant difference in overall stress duration between conditions, yoking did not work out properly. Participants were on average exposed to LESS stress in the uncontrollable condition but reported experiencing no difference or even MORE stress in this condition when asked afterwards. Therefore, effects showing greater performance decrements associated with uncontrollable stress cannot be attributed to more stress. Nevertheless, this remains a limitation.
3.3 Stressor Aversiveness
## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: aversiveness ~ condition * run.z + (1 + condition * run.z | id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 2514
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8211 -0.4516 -0.0157 0.5468 3.1717
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 211.25303 14.5345
## condition1 0.01676 0.1295 -0.29
## run.z 49.04422 7.0032 0.42 0.75
## condition1:run.z 0.12583 0.3547 0.19 0.88 0.97
## Residual 31.04981 5.5722
## Number of obs: 356, groups: id, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 63.8384 2.1871 43.8370 29.188 < 2e-16 ***
## condition1 -0.4305 0.2960 252.3147 -1.455 0.14700
## run.z -3.7088 1.0866 44.1234 -3.413 0.00139 **
## condition1:run.z -0.2085 0.3004 200.1551 -0.694 0.48850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1 run.z
## condition1 -0.019
## run.z 0.399 0.047
## cndtn1:rn.z 0.034 0.010 0.165
## convergence code: 0
## boundary (singular) fit: see ?isSingular## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: aversiveness ~ condition * run.z + (1 + re1.condition1 + re1.run.z +
## re1.condition1_by_run.z || id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 2523
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9451 -0.4570 -0.0275 0.5309 3.1533
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 2.101e+02 1.450e+01
## id.1 re1.condition1 3.370e-14 1.836e-07
## id.2 re1.run.z 4.907e+01 7.005e+00
## id.3 re1.condition1_by_run.z 0.000e+00 0.000e+00
## Residual 3.125e+01 5.590e+00
## Number of obs: 356, groups: id, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 63.8372 2.1816 43.8455 29.261 < 2e-16 ***
## condition1 -0.4354 0.2963 264.1050 -1.470 0.14286
## run.z -3.7103 1.0871 44.1177 -3.413 0.00139 **
## condition1:run.z -0.2151 0.2967 264.1050 -0.725 0.46901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1 run.z
## condition1 0.000
## run.z 0.001 0.000
## cndtn1:rn.z 0.000 0.000 0.000
## convergence code: 0
## boundary (singular) fit: see ?isSingular## Groups Name Std.Dev.
## id (Intercept) 1.4496e+01
## id.1 re1.condition1 1.8357e-07
## id.2 re1.run.z 7.0047e+00
## id.3 re1.condition1_by_run.z 0.0000e+00
## Residual 5.5899e+00## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: aversiveness ~ condition * run.z + (1 + re1.condition1 + re1.run.z ||
## id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 2523
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9451 -0.4570 -0.0275 0.5309 3.1533
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 210.14 14.496
## id.1 re1.condition1 0.00 0.000
## id.2 re1.run.z 49.07 7.005
## Residual 31.25 5.590
## Number of obs: 356, groups: id, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 63.8372 2.1816 43.8455 29.261 < 2e-16 ***
## condition1 -0.4354 0.2963 264.1050 -1.470 0.14286
## run.z -3.7103 1.0871 44.1177 -3.413 0.00139 **
## condition1:run.z -0.2151 0.2967 264.1050 -0.725 0.46901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1 run.z
## condition1 0.000
## run.z 0.001 0.000
## cndtn1:rn.z 0.000 0.000 0.000
## convergence code: 0
## boundary (singular) fit: see ?isSingular## Groups Name Std.Dev.
## id (Intercept) 14.4963
## id.1 re1.condition1 0.0000
## id.2 re1.run.z 7.0047
## Residual 5.5899## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: aversiveness ~ condition * run.z + (1 + run.z | id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 2515.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9268 -0.4541 -0.0162 0.5456 3.1957
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 211.18 14.532
## run.z 49.01 7.001 0.42
## Residual 31.24 5.590
## Number of obs: 356, groups: id, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 63.8377 2.1869 43.8362 29.191 < 2e-16 ***
## condition1 -0.4354 0.2962 264.1496 -1.470 0.14284
## run.z -3.7096 1.0865 44.1244 -3.414 0.00138 **
## condition1:run.z -0.2151 0.2967 264.1496 -0.725 0.46899
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1 run.z
## condition1 0.000
## run.z 0.398 0.000
## cndtn1:rn.z 0.000 0.000 0.000
## $id
## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Data: data
## Models:
## avers.m3c: aversiveness ~ condition * run.z + (1 + run.z | id)
## avers.maxc: aversiveness ~ condition * run.z + (1 + condition * run.z | id)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## avers.m3c 8 2535.5 2566.5 -1259.8 2519.5
## avers.maxc 15 2548.0 2606.2 -1259.0 2518.0 1.5074 7 0.9821| Fixed Effects - Stressor Aversiveness | |||
|---|---|---|---|
| Predictors | Estimates | 95% CI | p |
| Intercept | 63.84 | 59.55 – 68.12 | <0.001 |
| Condition | -0.44 | -1.02 – 0.15 | 0.143 |
| Run | -3.71 | -5.84 – -1.58 | 0.001 |
| Condition x Run | -0.22 | -0.80 – 0.37 | 0.469 |
| Random Effects | |||
| σ2 | 31.24 | ||
| τ00 id | 211.18 | ||
| τ11 id.run.z | 49.01 | ||
| ρ01 id | 0.42 | ||
| ICC | 0.89 | ||
| N id | 45 | ||
| Marginal R2 / Conditional R2 | 0.046 / 0.898 | ||
Participants rated the stressor as very aversive in both controllable and uncontrollable trials (global M = 63.75). In both conditions, aversiveness ratings decreased slightly over runs.
3.4 Perceived Control
## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: control ~ condition * run.z + (1 + condition * run.z | id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 3039.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3519 -0.4355 0.0359 0.4547 3.0173
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 130.72 11.433
## condition1 95.47 9.771 -0.06
## run.z 35.15 5.929 0.25 -0.03
## condition1:run.z 14.02 3.745 -0.16 0.60 -0.29
## Residual 164.98 12.845
## Number of obs: 356, groups: id, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 48.2227 1.8367 44.2129 26.255 < 2e-16 ***
## condition1 16.4509 1.6089 44.2375 10.225 3.15e-13 ***
## run.z 0.8853 1.1206 39.7904 0.790 0.4342
## condition1:run.z 1.8034 0.8850 44.3395 2.038 0.0476 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1 run.z
## condition1 -0.050
## run.z 0.190 -0.022
## cndtn1:rn.z -0.093 0.344 -0.146
## $id
| Fixed Effects - Perceived Control | |||
|---|---|---|---|
| Predictors | Estimates | 95% CI | p |
| Intercept | 48.22 | 44.62 – 51.82 | <0.001 |
| Condition | 16.45 | 13.30 – 19.60 | <0.001 |
| Run | 0.89 | -1.31 – 3.08 | 0.434 |
| Condition x Run | 1.80 | 0.07 – 3.54 | 0.048 |
| Random Effects | |||
| σ2 | 164.98 | ||
| τ00 id | 130.72 | ||
| τ11 id.condition1 | 95.47 | ||
| τ11 id.run.z | 35.15 | ||
| τ11 id.condition1:run.z | 14.02 | ||
| ρ01 | -0.06 | ||
| 0.25 | |||
| -0.16 | |||
| ICC | 0.63 | ||
| N id | 45 | ||
| Marginal R2 / Conditional R2 | 0.385 / 0.769 | ||
Participants reported higher perceived control under controllable trials compared to uncontrollable trials. Ratings increased over runs for controllable stress, but decreased for uncontrollable stress.
4 Ratings
4.1 Stress
## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: stress ~ condition + (1 + condition * run.z | id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 4264
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9127 -0.3974 -0.0433 0.3793 4.2869
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 269.274 16.410
## condition1 179.736 13.407 -0.23
## condition2 65.835 8.114 0.20 -0.78
## run.z 24.931 4.993 0.16 0.13 0.02
## condition1:run.z 7.861 2.804 -0.51 0.05 -0.12 -0.93
## condition2:run.z 2.471 1.572 0.22 -0.02 0.58 0.55 -0.53
## Residual 81.216 9.012
## Number of obs: 534, groups: id, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 38.663 2.356 44.138 16.411 < 2e-16 ***
## condition1 -17.691 2.051 44.049 -8.626 5.14e-11 ***
## condition2 7.437 1.299 44.626 5.723 8.24e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1
## condition1 -0.223
## condition2 0.186 -0.765
## convergence code: 0
## boundary (singular) fit: see ?isSingular## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## stress ~ condition + (1 + re1.condition1 + re1.condition2 + re1.run.z +
## re1.condition1_by_run.z + re1.condition2_by_run.z || id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 4323.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4502 -0.3849 -0.0329 0.3338 4.0466
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 266.691 16.331
## id.1 re1.condition1 162.838 12.761
## id.2 re1.condition2 58.278 7.634
## id.3 re1.run.z 24.409 4.941
## id.4 re1.condition1_by_run.z 5.207 2.282
## id.5 re1.condition2_by_run.z 0.000 0.000
## Residual 85.267 9.234
## Number of obs: 534, groups: id, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 36.930 2.468 44.014 14.967 < 2e-16 ***
## condition1 -18.246 1.985 43.951 -9.193 8.48e-12 ***
## condition2 7.110 1.271 44.057 5.595 1.32e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1
## condition1 0.000
## condition2 0.000 -0.063
## convergence code: 0
## boundary (singular) fit: see ?isSingular## Groups Name Std.Dev.
## id (Intercept) 16.3307
## id.1 re1.condition1 12.7608
## id.2 re1.condition2 7.6340
## id.3 re1.run.z 4.9406
## id.4 re1.condition1_by_run.z 2.2820
## id.5 re1.condition2_by_run.z 0.0000
## Residual 9.2340## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: stress ~ condition + (1 + condition + run.z | id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 4290.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5270 -0.3883 -0.0462 0.3709 4.1945
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 267.24 16.347
## condition1 180.09 13.420 -0.24
## condition2 64.51 8.032 0.20 -0.79
## run.z 23.98 4.897 0.14 0.15 -0.01
## Residual 89.41 9.456
## Number of obs: 534, groups: id, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 37.888 2.453 43.999 15.443 < 2e-16 ***
## condition1 -17.387 2.067 44.034 -8.412 1.04e-10 ***
## condition2 7.098 1.330 44.102 5.336 3.13e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1
## condition1 -0.244
## condition2 0.178 -0.748
## $id
## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Data: data
## Models:
## stress.m2c: stress ~ condition + (1 + condition + run.z | id)
## stress.maxc: stress ~ condition + (1 + condition * run.z | id)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## stress.m2c 14 4327.4 4387.3 -2149.7 4299.4
## stress.maxc 25 4322.3 4429.3 -2136.1 4272.3 27.104 11 0.004433 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## condition emmean SE df lower.CL upper.CL
## bas 20.5 2.86 44 14.7 26.3
## con 45.0 3.06 44 38.8 51.2
## noc 48.2 3.11 44 41.9 54.4
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95## contrast estimate SE df t.ratio p.value
## bas - con -24.48 3.26 44 -7.515 <.0001
## bas - noc -27.68 3.33 44 -8.300 <.0001
## con - noc -3.19 1.81 44 -1.765 0.0845
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: holm method for 3 tests| Fixed Effects - Stress | |||
|---|---|---|---|
| Predictors | Estimates | 95% CI | p |
| Intercept | 37.89 | 33.08 – 42.70 | <0.001 |
| Condition-CON | -17.39 | -21.44 – -13.34 | <0.001 |
| Condition-UNCON | 7.10 | 4.49 – 9.70 | <0.001 |
| Random Effects | |||
| σ2 | 89.41 | ||
| τ00 id | 267.24 | ||
| τ11 id.condition1 | 180.09 | ||
| τ11 id.condition2 | 64.51 | ||
| τ11 id.run.z | 23.98 | ||
| ρ01 | -0.24 | ||
| 0.20 | |||
| 0.14 | |||
| ICC | 0.81 | ||
| N id | 45 | ||
| Marginal R2 / Conditional R2 | 0.249 / 0.855 | ||
## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## stress ~ condition * aversiveness.z + (1 + condition * aversiveness.z +
## run | id) + (1 | run)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 2869.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9148 -0.3764 -0.0081 0.3585 3.8930
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 4.539e+02 2.131e+01
## condition1 1.365e+01 3.695e+00 -0.24
## aversiveness.z 4.617e+01 6.795e+00 -0.45 -0.64
## run 1.624e+01 4.030e+00 -0.61 0.31 0.44
## condition1:aversiveness.z 8.834e+00 2.972e+00 0.52 0.70 -0.95 -0.28
## run (Intercept) 5.836e-14 2.416e-07
## Residual 1.014e+02 1.007e+01
## Number of obs: 356, groups: id, 45; run, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 46.7521 2.6390 41.1145 17.716 < 2e-16 ***
## condition1 -2.3315 0.7898 42.4630 -2.952 0.00513 **
## aversiveness.z 4.9076 1.5789 23.0594 3.108 0.00494 **
## condition1:aversiveness.z 1.0953 0.7849 36.2080 1.395 0.17138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1 avrsv.
## condition1 -0.112
## aversvnss.z -0.179 -0.255
## cndtn1:vrs. 0.222 0.335 -0.467
## convergence code: 0
## boundary (singular) fit: see ?isSingularParticipants reported higher stress levels in both stress conditions compared to baseline. There was no difference in stress ratings between controllable and uncontrollable stress trials.
4.2 Helplessness
## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: helplessness ~ condition + (1 + condition * run.z | id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 3101.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.94525 -0.41873 -0.04023 0.40994 2.80271
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 309.894 17.604
## condition1 138.468 11.767 -0.13
## run.z 36.530 6.044 0.13 -0.12
## condition1:run.z 8.092 2.845 -0.28 0.47 0.03
## Residual 173.027 13.154
## Number of obs: 356, groups: id, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 46.389 2.676 44.049 17.337 < 2e-16 ***
## condition1 -10.338 1.831 44.006 -5.647 1.11e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## condition1 -0.076
## $id
| Fixed Effects - Helplessness | |||
|---|---|---|---|
| Predictors | Estimates | 95% CI | p |
| Intercept | 46.39 | 41.14 – 51.63 | <0.001 |
| Condition | -10.34 | -13.93 – -6.75 | <0.001 |
| Random Effects | |||
| σ2 | 173.03 | ||
| τ00 id | 309.89 | ||
| τ11 id.condition1 | 138.47 | ||
| τ11 id.run.z | 36.53 | ||
| τ11 id.condition1:run.z | 8.09 | ||
| ρ01 | -0.13 | ||
| 0.13 | |||
| -0.28 | |||
| ICC | 0.72 | ||
| N id | 45 | ||
| Marginal R2 / Conditional R2 | 0.147 / 0.763 | ||
Participants reported feeling less helpless in controllable trials compared to uncontrollable trials.
5 Reaction Times (RT)
## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: rt ~ condition + (1 + condition * run.z | id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 58677
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0075 -0.6585 -0.0145 0.6385 3.3779
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 2271.04 47.655
## condition1 302.99 17.406 -0.29
## condition2 70.71 8.409 0.27 -0.98
## run.z 69.52 8.338 0.24 0.18 -0.25
## condition1:run.z 124.89 11.175 0.19 -0.26 0.32 -0.40
## condition2:run.z 39.75 6.305 0.06 0.00 -0.16 0.82 -0.39
## Residual 10580.93 102.864
## Number of obs: 4830, groups: id, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 772.749 7.157 43.610 107.974 < 2e-16 ***
## condition1 18.834 3.591 42.188 5.244 4.75e-06 ***
## condition2 -27.254 2.405 61.588 -11.334 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1
## condition1 -0.146
## condition2 0.101 -0.723
## convergence code: 0
## boundary (singular) fit: see ?isSingular## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: rt ~ condition + (1 + re1.condition1 + re1.condition2 + re1.run +
## re1.condition1_by_run + re1.condition2_by_run || id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 58700.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9114 -0.6512 -0.0144 0.6349 3.2258
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 2153.106 46.402
## id.1 re1.condition1 110.125 10.494
## id.2 re1.condition2 0.000 0.000
## id.3 re1.run 55.606 7.457
## id.4 re1.condition1_by_run 1.105 1.051
## id.5 re1.condition2_by_run 0.000 0.000
## Residual 10716.215 103.519
## Number of obs: 4830, groups: id, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 773.808 7.414 43.343 104.368 < 2e-16 ***
## condition1 18.759 3.020 74.976 6.212 2.67e-08 ***
## condition2 -27.113 2.085 4699.506 -13.006 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1
## condition1 0.063
## condition2 -0.046 -0.514
## convergence code: 0
## boundary (singular) fit: see ?isSingular## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: rt ~ condition + (1 + re1.condition1 + re1.condition2 + re1.run ||
## id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 58700.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9107 -0.6510 -0.0143 0.6347 3.2269
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 2151.97 46.389
## id.1 re1.condition1 115.35 10.740
## id.2 re1.condition2 0.00 0.000
## id.3 re1.run 55.61 7.457
## Residual 10717.58 103.526
## Number of obs: 4830, groups: id, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 773.805 7.413 43.357 104.392 < 2e-16 ***
## condition1 18.750 3.014 78.577 6.221 2.24e-08 ***
## condition2 -27.110 2.085 4709.384 -13.003 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1
## condition1 0.063
## condition2 -0.046 -0.515
## convergence code: 0
## boundary (singular) fit: see ?isSingular## Groups Name Std.Dev.
## id (Intercept) 46.3894
## id.1 re1.condition1 10.7399
## id.2 re1.condition2 0.0000
## id.3 re1.run 7.4573
## Residual 103.5257## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: rt ~ condition + (1 + run | id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 58705.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9583 -0.6519 -0.0177 0.6412 3.1763
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 2491.75 49.917
## run 68.54 8.279 -0.26
## Residual 10768.09 103.769
## Number of obs: 4830, groups: id, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 772.625 7.415 44.147 104.195 < 2e-16 ***
## condition1 18.677 2.553 4744.938 7.317 2.96e-13 ***
## condition2 -27.056 2.089 4741.674 -12.951 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1
## condition1 0.075
## condition2 -0.046 -0.609
## $id
## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Data: data
## Models:
## rt.m3c: rt ~ condition + (1 + run | id)
## rt.maxc: rt ~ condition + (1 + condition * run.z | id)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## rt.m3c 7 58732 58777 -29359 58718
## rt.maxc 25 58740 58902 -29345 58690 28.014 18 0.06184 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## condition emmean SE df asymp.LCL asymp.UCL
## bas 791 8.02 Inf 776 807
## con 746 7.61 Inf 731 760
## noc 781 7.61 Inf 766 796
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95## contrast estimate SE df z.ratio p.value
## bas - con 45.7 4.17 Inf 10.972 <.0001
## bas - noc 10.3 4.18 Inf 2.466 0.0137
## con - noc -35.4 3.31 Inf -10.690 <.0001
##
## Degrees-of-freedom method: asymptotic
## P value adjustment: holm method for 3 tests| Fixed Effects - Reaction Times | |||
|---|---|---|---|
| Predictors | Estimates | 95% CI | p |
| Intercept | 772.62 | 758.09 – 787.16 | <0.001 |
| Condition-CON | 18.68 | 13.67 – 23.68 | <0.001 |
| Condition-UNCON | -27.06 | -31.15 – -22.96 | <0.001 |
| Random Effects | |||
| σ2 | 10768.09 | ||
| τ00 id | 2491.75 | ||
| τ11 id.run | 68.54 | ||
| ρ01 id | -0.26 | ||
| ICC | 0.19 | ||
| N id | 45 | ||
| Marginal R2 / Conditional R2 | 0.028 / 0.210 | ||
Reaction times were shorter under stress compared to baseline. Further, participants responded faster in the controllable condition compared to the uncontrollable condition.
6 Correct Responses
## Fitting 2 (g)lmer() models:
## [..]## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct ~ condition + (1 + condition * run.z | id)
## Data: data
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## AIC BIC logLik deviance df.resid
## 3654.0 3810.8 -1803.0 3606.0 5075
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.1084 0.2270 0.2946 0.3992 0.9212
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 0.493487 0.70249
## condition1 0.009456 0.09724 -0.36
## condition2 0.005575 0.07466 -0.82 0.59
## run.z 0.050323 0.22433 -0.18 -0.85 -0.17
## condition1:run.z 0.051431 0.22678 0.75 0.31 -0.54 -0.73
## condition2:run.z 0.061799 0.24859 -0.69 0.01 0.17 0.38 -0.49
## Number of obs: 5099, groups: id, 44
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.20180 0.13325 16.523 < 2e-16 ***
## condition1 -0.34078 0.09302 -3.664 0.000249 ***
## condition2 0.30334 0.08588 3.532 0.000412 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1
## condition1 -0.028
## condition2 -0.163 -0.538
## convergence code: 0
## boundary (singular) fit: see ?isSingular## Fitting 2 (g)lmer() models:
## [..]## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct ~ condition + (1 + re1.condition1 + re1.condition2 +
## re1.run.z + re1.condition1_by_run.z + re1.condition2_by_run.z || id)
## Data: data
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## AIC BIC logLik deviance df.resid
## 3638.3 3697.1 -1810.1 3620.3 5090
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0614 0.2322 0.3016 0.3987 0.8710
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.49878 0.7062
## id.1 re1.condition1 0.00000 0.0000
## id.2 re1.condition2 0.00000 0.0000
## id.3 re1.run.z 0.05822 0.2413
## id.4 re1.condition1_by_run.z 0.01614 0.1271
## id.5 re1.condition2_by_run.z 0.04772 0.2184
## Number of obs: 5099, groups: id, 44
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.08802 0.11806 17.686 < 2e-16 ***
## condition1 -0.32386 0.06730 -4.812 1.49e-06 ***
## condition2 0.30626 0.06293 4.867 1.13e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1
## condition1 0.053
## condition2 -0.006 -0.577
## convergence code: 0
## boundary (singular) fit: see ?isSingular## Groups Name Std.Dev.
## id (Intercept) 0.70625
## id.1 re1.condition1 0.00000
## id.2 re1.condition2 0.00000
## id.3 re1.run.z 0.24128
## id.4 re1.condition1_by_run.z 0.12706
## id.5 re1.condition2_by_run.z 0.21844## Fitting 2 (g)lmer() models:
## [..]## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct ~ condition + (1 + run.z | id)
## Data: data
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## AIC BIC logLik deviance df.resid
## 3636.6 3675.9 -1812.3 3624.6 5093
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.1134 0.2358 0.3061 0.3976 0.9264
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 0.49329 0.7023
## run.z 0.05608 0.2368 -0.09
## Number of obs: 5099, groups: id, 44
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.09322 0.13820 15.146 < 2e-16 ***
## condition1 -0.31162 0.06688 -4.659 3.17e-06 ***
## condition2 0.30358 0.06200 4.897 9.74e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1
## condition1 0.052
## condition2 -0.006 -0.582
## $id
## Fitting 2 (g)lmer() models:
## [..]## Fitting 2 (g)lmer() models:
## [..]## Data: data
## Models:
## corr.m2c: correct ~ condition + (1 + run.z | id)
## corr.maxc: correct ~ condition + (1 + condition * run.z | id)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## corr.m2c 6 3636.6 3675.9 -1812.3 3624.6
## corr.maxc 24 3654.0 3810.8 -1803.0 3606.0 18.672 18 0.4123## condition emmean SE df asymp.LCL asymp.UCL
## bas 1.78 0.157 Inf 1.47 2.09
## con 2.40 0.151 Inf 2.10 2.69
## noc 2.10 0.147 Inf 1.81 2.39
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95## contrast estimate SE df z.ratio p.value
## bas - con -0.615 0.115 Inf -5.367 <.0001
## bas - noc -0.320 0.110 Inf -2.908 0.0069
## con - noc 0.296 0.101 Inf 2.927 0.0069
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: holm method for 3 tests| Fixed Effects - Correct Responses | |||
|---|---|---|---|
| Predictors | Estimates | 95% CI | p |
| Intercept | 2.09 | 1.82 – 2.36 | <0.001 |
| Condition-CON | -0.31 | -0.44 – -0.18 | <0.001 |
| Condition-UNCON | 0.30 | 0.18 – 0.43 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 id | 0.49 | ||
| τ11 id.run.z | 0.06 | ||
| ρ01 id | -0.09 | ||
| ICC | 0.13 | ||
| N id | 44 | ||
| Marginal R2 / Conditional R2 | 0.014 / 0.142 | ||
Performance was significantly better (higher rate of correct responses) under stress compared to baseline. Further, participants responded correctly more often in the controllable condition compared to the uncontrollable condition.
7 Heart Rate (HR)
## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bpm ~ condition + (1 + condition * run | id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 2475
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8671 -0.4801 -0.0130 0.4876 6.6171
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 214.6552 14.6511
## condition1 1.3146 1.1465 -0.87
## condition2 3.5769 1.8913 0.75 -0.98
## run 6.9078 2.6283 -0.68 0.95 -0.99
## condition1:run 0.1157 0.3402 0.98 -0.95 0.86 -0.80
## condition2:run 0.3544 0.5953 -0.78 0.98 -1.00 0.99 -0.88
## Residual 5.9750 2.4444
## Number of obs: 462, groups: id, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 62.4823 1.6336 41.3321 38.247 <2e-16 ***
## condition1 -0.2225 0.1631 288.0715 -1.364 0.174
## condition2 0.2088 0.1619 364.2497 1.290 0.198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1
## condition1 0.122
## condition2 -0.009 -0.503
## convergence code: 0
## boundary (singular) fit: see ?isSingular## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bpm ~ condition + (1 + re1.condition1 + re1.condition2 + re1.run.z +
## re1.condition1_by_run.z + re1.condition2_by_run.z || id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 2497.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4961 -0.4922 -0.0164 0.4284 6.5400
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 1.217e+02 1.103e+01
## id.1 re1.condition1 0.000e+00 0.000e+00
## id.2 re1.condition2 2.024e-14 1.423e-07
## id.3 re1.run.z 8.182e+00 2.860e+00
## id.4 re1.condition1_by_run.z 0.000e+00 0.000e+00
## id.5 re1.condition2_by_run.z 0.000e+00 0.000e+00
## Residual 6.353e+00 2.521e+00
## Number of obs: 462, groups: id, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 65.0530 1.7067 41.0029 38.117 < 2e-16 ***
## condition1 -0.5347 0.1658 375.7917 -3.224 0.00138 **
## condition2 0.5405 0.1658 375.7917 3.259 0.00122 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1
## condition1 0.000
## condition2 0.000 -0.500
## convergence code: 0
## boundary (singular) fit: see ?isSingular## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bpm ~ condition + (1 + re1.condition1 + re1.condition2 + re1.run ||
## id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 2506.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5784 -0.4898 -0.0148 0.4131 6.5227
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 1.496e+02 1.223e+01
## id.1 re1.condition1 0.000e+00 0.000e+00
## id.2 re1.condition2 1.346e-16 1.160e-08
## id.3 re1.run 6.614e+00 2.572e+00
## Residual 6.370e+00 2.524e+00
## Number of obs: 462, groups: id, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 69.9501 1.9112 40.9847 36.600 < 2e-16 ***
## condition1 -0.5347 0.1661 373.8711 -3.220 0.00140 **
## condition2 0.5405 0.1661 373.8711 3.255 0.00124 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1
## condition1 0.000
## condition2 0.000 -0.500
## convergence code: 0
## boundary (singular) fit: see ?isSingular## Groups Name Std.Dev.
## id (Intercept) 1.2231e+01
## id.1 re1.condition1 0.0000e+00
## id.2 re1.condition2 1.1602e-08
## id.3 re1.run 2.5717e+00
## Residual 2.5239e+00## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bpm ~ condition + (1 + run | id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 2495.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4130 -0.4908 -0.0258 0.4270 6.5567
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 218.602 14.785
## run 6.776 2.603 -0.69
## Residual 6.353 2.521
## Number of obs: 462, groups: id, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 62.1988 1.6732 41.1307 37.174 < 2e-16 ***
## condition1 -0.5347 0.1658 375.8260 -3.224 0.00137 **
## condition2 0.5405 0.1658 375.8260 3.259 0.00122 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1
## condition1 0.000
## condition2 0.000 -0.500
## $id
## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Data: data
## Models:
## hr.m3c: bpm ~ condition + (1 + run | id)
## hr.maxc: bpm ~ condition + (1 + condition * run | id)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## hr.m3c 7 2508.8 2537.8 -1247.4 2494.8
## hr.maxc 25 2523.8 2627.2 -1236.9 2473.8 20.986 18 0.2801## condition emmean SE df lower.CL upper.CL
## bas 61.7 1.72 41.8 58.2 65.1
## con 62.7 1.72 41.8 59.3 66.2
## noc 62.2 1.72 41.8 58.7 65.7
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95## contrast estimate SE df t.ratio p.value
## bas - con -1.075 0.287 376 -3.743 0.0006
## bas - noc -0.529 0.287 376 -1.841 0.1159
## con - noc 0.546 0.287 376 1.902 0.1159
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: holm method for 3 tests| Fixed Effects - Heart Rate | |||
|---|---|---|---|
| Predictors | Estimates | 95% CI | p |
| Intercept | 62.20 | 58.92 – 65.48 | <0.001 |
| Condition-CON | -0.53 | -0.86 – -0.21 | 0.001 |
| Condition-UNCON | 0.54 | 0.22 – 0.87 | 0.001 |
| Random Effects | |||
| σ2 | 6.35 | ||
| τ00 id | 218.60 | ||
| τ11 id.run | 6.78 | ||
| ρ01 id | -0.69 | ||
| ICC | 0.97 | ||
| N id | 42 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.972 | ||
Heart rates were higher under controllable stress compared to baseline. No other contrasts were significant.
8 MR Parameter Estimates
8.1 Does vmPFC activation modulate ratings?
## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: stress ~ condition * beta_weight.z + (1 + condition + run.z | id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 2734
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2382 -0.3921 -0.0011 0.3647 3.9295
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 349.28 18.689
## condition1 25.34 5.034 0.01
## run.z 34.63 5.884 0.36 0.18
## Residual 102.65 10.132
## Number of obs: 336, groups: id, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 49.6090 2.8049 41.2989 17.686 <2e-16 ***
## condition1 -1.3897 0.9560 42.0390 -1.454 0.153
## beta_weight.z -0.4732 0.8183 278.5410 -0.578 0.564
## condition1:beta_weight.z 0.2263 0.6945 263.5330 0.326 0.745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1 bt_wg.
## condition1 -0.037
## beta_wght.z 0.039 -0.145
## cndtn1:bt_. -0.047 -0.007 0.041## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: helplessness ~ condition * beta_weight.z + (1 + condition * run.z |
## id)
## Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 10000))
##
## REML criterion at convergence: 2916.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.92315 -0.43201 -0.04629 0.39820 2.85392
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 305.192 17.470
## condition1 134.433 11.595 -0.06
## run.z 33.159 5.758 0.23 -0.26
## condition1:run.z 7.446 2.729 -0.16 0.37 0.15
## Residual 174.531 13.211
## Number of obs: 336, groups: id, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 47.48103 2.74810 41.50394 17.278 < 2e-16 ***
## condition1 -10.96352 1.87977 42.26682 -5.832 6.78e-07 ***
## beta_weight.z 0.02837 1.05541 275.34053 0.027 0.9786
## condition1:beta_weight.z 2.04439 0.98787 244.65767 2.069 0.0395 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndtn1 bt_wg.
## condition1 -0.009
## beta_wght.z 0.024 -0.137
## cndtn1:bt_. -0.085 0.031 0.065| Fixed Effects - Helplessness | |||
|---|---|---|---|
| Predictors | Estimates | 95% CI | p |
| Intercept | 47.48 | 42.09 – 52.87 | <0.001 |
| Condition | -10.96 | -14.65 – -7.28 | <0.001 |
| Beta Weight | 0.03 | -2.04 – 2.10 | 0.979 |
| Condition x Beta Weight | 2.04 | 0.11 – 3.98 | 0.040 |
| Random Effects | |||
| σ2 | 174.53 | ||
| τ00 id | 305.19 | ||
| τ11 id.condition1 | 134.43 | ||
| τ11 id.run.z | 33.16 | ||
| τ11 id.condition1:run.z | 7.45 | ||
| ρ01 | -0.06 | ||
| 0.23 | |||
| -0.16 | |||
| ICC | 0.72 | ||
| N id | 42 | ||
| Marginal R2 / Conditional R2 | 0.169 / 0.764 | ||
VmPFC activation modulated helplessness ratings for uncontrollable stress, not for controllable stress. For uncontrollable stress, higher beta weights were linked to lower helplessness ratings.
9 Correction for Multiple Dependent Variables
## DV p.orig Bonferroni
## 2 helplessness 0.0001 0.0005
## 3 RT 0.0001 0.0005
## 4 correct 0.0069 0.0345
## 1 stress 0.0845 0.4225
## 5 hr 0.1159 0.579510 Figures for Paper
